The metamorphosis of medical imaging data from computed tomography (CT) and magnetic resonance imaging (MRI) into 3D models signifies a pronounced advancement in medical diagnostics, therapeutic planning, and personalized medicine. This process consolidates transforming high-resolution tomographic datasets into 3D anatomical structures through advanced segmentation techniques, such as thresholding, region-growing, and machine learning algorithms. Specially designed software approaching Mimics, 3D Slicer, and OsiriX enables the accurate rendering of these 3D models, essential for visualizing complex anatomical relationships. Furthermore, assimilating virtual and augmented reality (VR/AR) with these models enhances tissue engineering by enabling immersive simulations and real-time visualization for scaffold design and biomaterial interaction. These models also find applications in 3D printing for individual-specific scaffolds, implants, and bioprinted tissues, enabling 3D printing for preoperative planning, surgical rehearsals, and regenerative medicine while assimilating with virtual and augmented reality (VR/AR) environments for progressive diagnostic analysis and real-time surgical navigation. Additionally, 3D modeling is crucial in individual-specific implant design, rehabilitation, and custom devising for unique anatomical structures. Deep learning-based generative modeling and medical image synthesis further enhance the efficacy of 3D models by assisting the translation between numerous imaging modalities, i.e., CT, MRI, etc., reducing the need for multiple scans and enhancing clinical workflows. This convergence of imaging, computational modeling, and artificial intelligence (AI) is driving innovation in healthcare, improving diagnostic precision, surgical outcomes, and medical device development, demonstrating a new era in personalized individual care.